SoroushVahidi/scidocs huggingface dataset:
Approach and Avoidance Achievement Goals and Intrinsic Motivation : A Mediational Analysis
(2001)

Most contemporary achievement goal conceptualizations consist of a performance goal versus mastery goal dichotomy. The present research offers an alternative framework by partitioning the performance goal orientation into independent approach and avoidance motivational orientations. Two experiments investigated the predictive utility of the proposed approach-avoidance achievement goal conceptualization in the intrinsic motivation domain. Results from both experiments supported the proposed framework; only performance goals grounded in the avoidance of failure undermined intrinsic motivation. Task involvement was validated as a mediator of the observed effects on intrinsic motivation. Ramifications for the achievement goal approach to achievement motivation and future research avenues are discussed.
Intrinsic cancer subtypes-next steps into personalized medicine
(2014)

Recent technological advances have significantly improved our understanding of tumor biology by means of high-throughput mutation and transcriptome analyses. The application of genomics has revealed the mutational landscape and the specific deregulated pathways in different tumor types. At a transcriptional level, multiple gene expression signatures have been developed to identify biologically distinct subgroups of tumors. By supervised analysis, several prognostic signatures have been generated, some of them being commercially available. However, an unsupervised approach is required to discover a priori unknown molecular subtypes, the so-called intrinsic subtypes. Moreover, an integrative analysis of the molecular events associated with tumor biology has been translated into a better tumor classification. This molecular characterization confers new opportunities for therapeutic strategies in the management of cancer patients. However, the applicability of these new molecular classifications is limited because of several issues such as technological validation and cost. Further comparison with well-established clinical and pathological features is expected to accelerate clinical translation. In this review, we will focus on the data reported on molecular classification in the most common tumor types such as breast, colorectal and lung carcinoma, with special emphasis on recent data regarding tumor intrinsic subtypes. Likewise, we will review the potential applicability of these new classifications in the clinical routine.
3-D Deep Learning Approach for Remote Sensing Image Classification
(2018)

Recently, a variety of approaches have been enriching the field of remote sensing (RS) image processing and analysis. Unfortunately, existing methods remain limited to the rich spatiospectral content of today’s large data sets. It would seem intriguing to resort to deep learning (DL)-based approaches at this stage with regard to their ability to offer accurate semantic interpretation of the data. However, the specificity introduced by the coexistence of spectral and spatial content in the RS data sets widens the scope of the challenges presented to adapt DL methods to these contexts. Therefore, the aim of this paper is first to explore the performance of DL architectures for the RS hyperspectral data set classification and second to introduce a new 3-D DL approach that enables a joint spectral and spatial information process. A set of 3-D schemes is proposed and evaluated. Experimental results based on well-known hyperspectral data sets demonstrate that the proposed method is able to achieve a better classification rate than state-of-the-art methods with lower computational costs.
Towards Know-how Mapping Using Goal Modeling
(2013)

In organizing the knowledge in a field of study, it is common to use classification techniques to organize concepts and approaches along dimensions of interest. In technology domains, an advance often appears in the form of a new way or method for achieving an objective. This paper proposes to use goal modeling to map the means-ends knowledge (“know-how”) in a domain. A know-how map highlights the structure of recognized problems and known solutions in the domain, thus facilitating gap identification and prompting new research and innovation. We contrast the proposed goal-oriented approach with a claim-oriented approach, using Web Page Ranking as a sample domain.
Controlling Home Appliances Remotely through Voice Command
(2011)

the main concern in systems development is the integration of technologies to increase customer satisfaction. Research presented in this paper focuses mainly in three things first to understand the speech or voice of user second is to control the home appliances through voice call and third is to finds intrusion in the house. The user can make a voice call in order to perform certain actions such as switching lights on/off, getting the status of any appliance etc. And when system finds intrusion it sends an alert voice message to preconfigured cell when the user is away from the place. The proposed system is implemented using voice Global System for Mobile Communications (GSM) and wireless technology based on .NET framework and Attention (AT) commands. Microsoft speech reorganization engine, speech SDK 5.1 is used to understand the voice command of user. As it is wireless so more cost effective and easy to use. The GSM technology used in system provide the everywhere access of the system for security. Experimental results show that the system is more secure and cost effective as compared to existing systems. We conclude that this system provides solution for the problems faced by home owner in daily life and make their life easy and comfortable by proposing cost effective and reliable solution. Keywords-component; Voice GSM; Voice message; radio friquencey (RF); AT commands.
RFID Coverage Extension Using Microstrip-Patch Antenna Array [Wireless Corner]
(2009)

In this paper, a UHF-band 2 times 2 microstrip phased-array antenna is designed and implemented to extend the coverage of an RFID reader system. The phased-array antenna has four microstrip-patch antennas, three Wilkinson power dividers, and a transmission-line phase shifter. These are printed on a dielectric substrate with a dielectric constant of 4.5. The array has dimensions of 34 cm times 45 cm, operating at a frequency of 867 MHz, as specified in RFID Gen2 protocol European standards. The phased-array antenna has a measured directivity of 12.1 dB, and the main-beam direction can be steered to angles of plusmn 40deg, with a HPBW of 90deg. The phased-array antenna is used as the receiving antenna in a commercial reader system. Experimental results indicate that the coverage of the RFID system with the phased-array antenna is superior to the coverage with a conventional broader-beamwidth microstrip-patch antenna. The proposed system can also be used for a wireless positioning system.
N-Fold Inspection: A Requirements Analysis Technique
(1990)

N-fold inspection uses traditional inspections of the user requirements document (URD) but replicates the inspection activities using N independent teams. A pilot study was conducted to explore the usefulness of N-fold inspection during requirements analysis. A comparison of N-fold inspection with other development techniques reveals that N-fold inspection is a cost-effective method for finding faults in the URD and may be a valid technique in the development of mission-critical software systems.
A Benchmark for Open Link Prediction
(2018)

Knowledge graphs (KG) and open information extraction systems (OIE) both represent information by a set of (subject, relation, object)-triples. In KGs, arguments and relations are taken from a closed vocabulary, i.e., entities are disambiguated and relations are assumed to be semantically distinct. In OIE, arguments and relations are extracted from natural language text. There is no predefined or closed vocabulary, many different mentions may refer to the same entity, and relations can be semantically close but not equal, e.g. “visited by” and “frequently visited by”. The set of OIE triples can be viewed as an open knowledge graph (OKG). In this paper, we study the problem of open link prediction: Given a mention of an entity and an open relation (e.g., “A. Einstein” and “is a native of”), predict correct entity mentions (e.g., “Imperial Germany”) using solely the OKG. Models that aim to solve the open link prediction task sucessfully need to be able to reason about different entity mentions, as well as the relations between them, in a fully automatic, unsupervised way. As a benchmark for this task we design an OKG dataset, an evaluation protocol and present and evaluate a number of baseline models. In particular, we compare a token-based co-occurrence model, a non token-based link prediction model, and several token-based link prediction models. Our results indicate that the latter combination is beneficial.
Compressive sensing
(2008)

1 Scope The Shannon/Nyquist sampling theorem tells us that in order to not lose information when uniformly sampling a signal we must sample at least two times faster than its bandwidth. In many applications, including digital image and video cameras, the Nyquist rate can be so high that we end up with too many samples and must compress in order to store or transmit them. In other applications, including imaging systems (medical scanners, radars) and high-speed analog-to-digital converters, increasing the sampling rate or density beyond the current state-of-the-art is very expensive. In this lecture, we will learn about a new technique that tackles these issues using compressive sensing [1, 2]. We will replace the conventional sampling and reconstruction operations with a more general linear measurement scheme coupled with an optimization in order to acquire certain kinds of signals at a rate significantly below Nyquist.
Formal Specification with the Java Modeling Language
(2016)

This text is a general, self contained, and tool independent introduction into the Java Modeling Language, JML. It appears in a book about the KeY approach and tool, because JML is the dominating starting point of KeY style Java verification. However, this chapter does not depend on KeY, nor any other specific tool, nor on any specific verification methodology. With this text, the authors aim to provide, for the time being, the definitive, general JML tutorial. Other chapters in this book discuss the particular usage of JML in KeY style verification.1 In this chapter, however, we only refer to KeY in very few places, without relying on it. This introduction is written for all readers with an interest in formal specification of software in general, and anyone who wants to learn about the JML approach to specification in particular. A preliminary version of this chapter appeared as a technical report [Huisman et al., 2014].
Low complexity photo sensor dead pixel detection algorithm
(2012)

Although the number of pixels in image sensors is increasing exponentially, production techniques have only been able to linearly reduce the probability that a pixel will be defective. The result is a rapidly increasing probability that a sensor will contain one or more defective pixels. The defect pixel detection and defect pixel correction are operated separately but the former must employ before the latter is in use. Traditional detection scheme, which finds the defect pixels during manufacturing, is not able to discover the spread defect pixels years late. Consequently, the lifetime and robust defect pixel detection technique, which identifies the fault pixels when camera is in use, is more practical and developed. The paper presents a two stages dead pixel detection technique without complicated mathematic computations so that the embedded devices can easily implement it. Using six dead pixel types are tested and the experimental result indicates that it can be accelerated more than four times the detection time.
Probabilistic Latent Semantic Indexing
(1999)

Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized model is able to deal with domain{specific synonymy as well as with polysemous words. In contrast to standard Latent Semantic Indexing (LSI) by Singular Value Decomposition, the probabilistic variant has a solid statistical foundation and defines a proper generative data model. Retrieval experiments on a number of test collections indicate substantial performance gains over direct term matching methods as well as over LSI. In particular, the combination of models with different dimensionalities has proven to be advantageous.
Latent Semantic Indexing (LSI): TREC-3 Report
(1994)

We used LSI for both TREC-3 routing and adhoc tasks. For the routing tasks an LSI space was constructed using the training documents. We compared profiles constructed using just the topic words (no training) with profiles constructed using the average of relevant documents (no use of the topic words). Not surprisingly, the centroid of the relevant documents was 30% better than the topic words. This simple feedback method was quite good compared to the routing performance of other systems. Various combinations of information from the topic words and relevant documents provide small additional improvements in performance. For the adhoc task we compared LSI to keyword vector matching (i.e. using no dimension reduction). Small advantages were obtained for LSI even with the long TREC topic statements.
Highly Efficient Mining of Overlapping Clusters in Signed Weighted Networks
(2017)

In many practical contexts, networks are weighted as their links are assigned numerical weights representing relationship strengths or intensities of inter-node interaction. Moreover, the links' weight can be positive or negative, depending on the relationship or interaction between the connected nodes. The existing methods for network clustering however are not ideal for handling very large signed weighted networks. In this paper, we present a novel method called LPOCSIN (short for "Linear Programming based Overlapping Clustering on Signed Weighted Networks") for efficient mining of overlapping clusters in signed weighted networks. Different from existing methods that rely on computationally expensive cluster cohesiveness measures, LPOCSIN utilizes a simple yet effective one. Using this measure, we transform the cluster assignment problem into a series of alternating linear programs, and further propose a highly efficient procedure for solving those alternating problems. We evaluate LPOCSIN and other state-of-the-art methods by extensive experiments covering a wide range of synthetic and real networks. The experiments show that LPOCSIN significantly outperforms the other methods in recovering ground-truth clusters while being an order of magnitude faster than the most efficient state-of-the-art method.
A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification
(2009)

A scheme for the automatic detection of nodules in thoracic computed tomography scans is presented and extensively evaluated. The algorithm uses the local image features of shape index and curvedness in order to detect candidate structures in the lung volume and applies two successive k-nearest-neighbour classifiers in the reduction of false-positives. The nodule detection system is trained and tested on three databases extracted from a large-scale experimental screening study. The databases are constructed in order to evaluate the algorithm on both randomly chosen screening data as well as data containing higher proportions of nodules requiring follow-up. The system results are extensively evaluated including performance measurements on specific nodule types and sizes within the databases and on lesions which later proved to be malignant. In a random selection of 813 scans from the screening study a sensitivity of 80% with an average 4.2 false-positives per scan is achieved. The detection results presented are a realistic measure of a CAD system performance in a low-dose screening study which includes a diverse array of nodules of many varying sizes, types and textures.
The Software Concordance: A User Interface for Advanced Software Documents
(2002)

The Software Concordance is a hypermedia software development environment exploring how document technology and versioned hypermedia can improve software document management. The Software Concordance’s central tool is a document editor that integrates program analysis and hypermedia services for both source code and multimedia documentation in XML. The editor allows developers to embed inline multimedia documentation, including images and audio clips, into their program sources and to bind them to any program fragment. Web style hyperlinks are also supported. The developers are able to move seamlessly between source code and the documentation that describes its motivation, design, correctness and use without disrupting the integrated program analysis services, which include lexing, parsing and type checking. This paper motivates the need for environments like the Software Concordance, describes the key features and architecture of its document editor and discusses future work to improve its functionality.
Cloud-enabled privacy-preserving collaborative learning for mobile sensing
(2012)

In this paper, we consider the design of a system in which Internet-connected mobile users contribute sensor data as training samples, and collaborate on building a model for classification tasks such as activity or context recognition. Constructing the model can naturally be performed by a service running in the cloud, but users may be more inclined to contribute training samples if the privacy of these data could be ensured. Thus, in this paper, we focus on privacy-preserving collaborative learning for the mobile setting, which addresses several competing challenges not previously considered in the literature: supporting complex classification methods like support vector machines, respecting mobile computing and communication constraints, and enabling user-determined privacy levels. Our approach, Pickle, ensures classification accuracy even in the presence of significantly perturbed training samples, is robust to methods that attempt to infer the original data or poison the model, and imposes minimal costs. We validate these claims using a user study, many real-world datasets and two different implementations of Pickle.
Malware Detection Using Deep Transferred Generative Adversarial Networks
(2017)

Malicious software is generated with more and more modified features of which the methods to detect malicious software use characteristics. Automatic classification of malicious software is efficient because it does not need to store all characteristic. In this paper, we propose a transferred generative adversarial network (tGAN) for automatic classification and detection of the zero-day attack. Since the GAN is unstable in training process, often resulting in generator that produces nonsensical outputs, a method to pre-train GAN with autoencoder structure is proposed. We analyze the detector, and the performance of the detector is visualized by observing the clustering pattern of malicious software using t-SNE algorithm. The proposed model gets the best performance compared with the conventional machine learning algorithms.
Example-based hinting of true type fonts
(2000)

Hinting in TrueType is a time-consuming manual process in which a typographer creates a sequence of instructions for better fitting the characters of a font to a grid of pixels. In this paper, we propose a new method for automatically hinting TrueType fonts by transferring hints of one font to another. Given a hinted source font and a target font without hints, our method matches the outlines of corresponding glyphs in each font, and then translates all of the individual hints for each glyph from the source to the target font. It also translates the control value table (CVT) entries, which are used to unify feature sizes across a font. The resulting hinted font already provides a great improvement over the unhinted version. More importantly, the translated hints, which preserve the sound, hand-designed hinting structure of the original font, provide a very good starting point for a professional typographer to complete and fine-tune, saving time and increasing productivity. We demonstrate our approach with examples of automatically hinted fonts at typical display sizes and screen resolutions. We also provide estimates of the time saved by a professional typographer in hinting new fonts using this semi-automatic approach.
Learning Progressions: Supporting Instruction and Formative Assessment Margaret Heritage the Council of Chief State School Officers T He Fast Scass  Formative Assessment for Teachers and Learners Learning Progressions: Supporting Instruction and Formative Assessment
(2008)

By its very nature, learning involves progression. To assist in its emergence, teachers need to understand the pathways along which students are expected to progress. These pathways or progressions ground both instruction and assessment. Yet, despite a plethora of standards and curricula, many teachers are unclear about how learning progresses in specific domains. This is an undesirable situation for teaching and learning, and one that particularly affects teachers’ ability to engage in formative assessment.
Corporate Social Responsibility and Innovation in Management Accounting
(2013)

• This study develops a conceptual framework to illustrate how sustainability issues are embedded in the management control system to operationalise firms' corporate social responsibility (CSR) objectives and strategies. • Categorising firms' CSR activities into Responsive and Strategic CSR agendas (Porter and Kramer 2006), this framework emphasises that different uses of MCS must be adopted to effectively operationalise different CSR agendas. • Boundary systems and diagnostic uses of budgets and performance management systems are more pertinent to operationalising Responsive CSR agendas, whereas the belief system and interactive uses of MCS are more effective in facilitating the selection and implementation of Strategic CSR programmes. • This study provides empirical evidence on the different types of Responsive and Strategic CSR programmes in two Chinese State-owned Enterprises (SOEs). Our empirical data shed light on the role of MCS in implementing CSR strategies at these firms.
A High Gain Input-Parallel Output-Series DC/DC Converter With Dual Coupled Inductors
(2015)

High voltage gain dc-dc converters are required in many industrial applications such as photovoltaic and fuel cell energy systems, high-intensity discharge lamp (HID), dc back-up energy systems, and electric vehicles. This paper presents a novel input-parallel output-series boost converter with dual coupled inductors and a voltage multiplier module. On the one hand, the primary windings of two coupled inductors are connected in parallel to share the input current and reduce the current ripple at the input. On the other hand, the proposed converter inherits the merits of interleaved series-connected output capacitors for high voltage gain, low output voltage ripple, and low switch voltage stress. Moreover, the secondary sides of two coupled inductors are connected in series to a regenerative capacitor by a diode for extending the voltage gain and balancing the primary-parallel currents. In addition, the active switches are turned on at zero current and the reverse recovery problem of diodes is alleviated by reasonable leakage inductances of the coupled inductors. Besides, the energy of leakage inductances can be recycled. A prototype circuit rated 500-W output power is implemented in the laboratory, and the experimental results shows satisfactory agreement with the theoretical analysis.